'''simple docstring'''




import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
  import os

  import jax.numpy as jnp
  from jax import jit

  from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
  from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

  __UpperCAmelCase					="0.12"  # assumed parallelism: 8


if is_torch_available():
  import torch






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=None							)      ->       Any:
    if rng is None:
        __lowerCamelCase            =    random.Random()

    __lowerCamelCase            =    1
    for dim in shape:
        total_dims *= dim

    __lowerCamelCase            =    []
    for _ in range(UpperCamelCase__							):
        values.append(rng.randint(0							,    vocab_size - 1							)							)

    __lowerCamelCase            =    np.array(UpperCamelCase__							,    dtype=jnp.intaa							).reshape(UpperCamelCase__							)

    return output






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=None							)      ->       int:
    __lowerCamelCase            =    ids_tensor(UpperCamelCase__							,    vocab_size=2							,    rng=UpperCamelCase__							)
    # make sure that at least one token is attended to for each batch
    __lowerCamelCase            =    1
    return attn_mask




@require_flax
class        a__ :
     lowerCamelCase    :	Tuple				=None
     lowerCamelCase    :	Optional[Any]				=()






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.model_tester.prepare_config_and_inputs_for_common()

         # cut to half length & take max batch_size 3
         __lowerCamelCase            =    2
         __lowerCamelCase            =    inputs['''input_ids'''].shape[-1] // 2
         __lowerCamelCase            =    inputs['''input_ids'''][:max_batch_size, :sequence_length]

         __lowerCamelCase            =    jnp.ones_like(a       )
         __lowerCamelCase            =    attention_mask[:max_batch_size, :sequence_length]

         # generate max 5 tokens
         __lowerCamelCase            =    input_ids.shape[-1] + 5
         if config.eos_token_id is not None and config.pad_token_id is None:
             # hack to allow generate for models such as GPT2 as is done in `generate()`
             __lowerCamelCase            =    config.eos_token_id
         return config, input_ids, attention_mask, max_length






     @is_pt_flax_cross_test
     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_input_ids_and_config()
         __lowerCamelCase            =    False
         __lowerCamelCase            =    max_length
         __lowerCamelCase            =    0

         for model_class in self.all_generative_model_classes:
             __lowerCamelCase            =    model_class(a       )

             __lowerCamelCase            =    model_class.__name__[4:]  # Skip the "Flax" at the beginning
             __lowerCamelCase            =    getattr(a    ,							a       )
             __lowerCamelCase            =    pt_model_class(a       ).eval()
             __lowerCamelCase            =    load_flax_weights_in_pytorch_model(a    ,							flax_model.params       )

             __lowerCamelCase            =    flax_model.generate(a       ).sequences
             __lowerCamelCase            =    pt_model.generate(torch.tensor(a    ,							dtype=torch.long       )       )

             if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                 __lowerCamelCase            =    flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

             self.assertListEqual(pt_generation_outputs.numpy().tolist()    ,							flax_generation_outputs.tolist()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_input_ids_and_config()
         __lowerCamelCase            =    False
         __lowerCamelCase            =    max_length

         for model_class in self.all_generative_model_classes:
             __lowerCamelCase            =    model_class(a       )

             __lowerCamelCase            =    model.generate(a       ).sequences
             self.assertEqual(generation_outputs.shape[-1]    ,							a       )

             __lowerCamelCase            =    jit(model.generate       )
             __lowerCamelCase            =    jit_generate(a       ).sequences

             self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_input_ids_and_config()
         __lowerCamelCase            =    True
         __lowerCamelCase            =    max_length

         for model_class in self.all_generative_model_classes:
             __lowerCamelCase            =    model_class(a       )

             __lowerCamelCase            =    model.generate(a       ).sequences
             self.assertEqual(generation_outputs.shape[-1]    ,							a       )

             __lowerCamelCase            =    jit(model.generate       )
             __lowerCamelCase            =    jit_generate(a       ).sequences

             self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_input_ids_and_config()
         __lowerCamelCase            =    False
         __lowerCamelCase            =    max_length
         __lowerCamelCase            =    2

         for model_class in self.all_generative_model_classes:
             __lowerCamelCase            =    model_class(a       )

             __lowerCamelCase            =    model.generate(a       ).sequences
             self.assertEqual(generation_outputs.shape[-1]    ,							a       )

             __lowerCamelCase            =    jit(model.generate       )
             __lowerCamelCase            =    jit_generate(a       ).sequences

             self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_input_ids_and_config()
         __lowerCamelCase            =    False
         __lowerCamelCase            =    max_length
         __lowerCamelCase            =    2
         __lowerCamelCase            =    2

         for model_class in self.all_generative_model_classes:
             __lowerCamelCase            =    model_class(a       )

             __lowerCamelCase            =    model.generate(a       ).sequences
             self.assertEqual(generation_outputs.shape[0]    ,							input_ids.shape[0] * config.num_return_sequences       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_input_ids_and_config()
         __lowerCamelCase            =    True
         __lowerCamelCase            =    max_length
         __lowerCamelCase            =    0.8
         __lowerCamelCase            =    10
         __lowerCamelCase            =    0.3
         __lowerCamelCase            =    1
         __lowerCamelCase            =    8
         __lowerCamelCase            =    9

         for model_class in self.all_generative_model_classes:
             __lowerCamelCase            =    model_class(a       )

             __lowerCamelCase            =    model.generate(a       ).sequences
             self.assertEqual(generation_outputs.shape[-1]    ,							a       )

             __lowerCamelCase            =    jit(model.generate       )
             __lowerCamelCase            =    jit_generate(a       ).sequences

             self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_input_ids_and_config()
         __lowerCamelCase            =    max_length
         __lowerCamelCase            =    1
         __lowerCamelCase            =    8
         __lowerCamelCase            =    9

         for model_class in self.all_generative_model_classes:
             __lowerCamelCase            =    model_class(a       )

             __lowerCamelCase            =    model.generate(a       ).sequences
             self.assertEqual(generation_outputs.shape[-1]    ,							a       )

             __lowerCamelCase            =    jit(model.generate       )
             __lowerCamelCase            =    jit_generate(a       ).sequences

             self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_input_ids_and_config()
         __lowerCamelCase            =    max_length
         __lowerCamelCase            =    2
         __lowerCamelCase            =    1
         __lowerCamelCase            =    8
         __lowerCamelCase            =    9

         for model_class in self.all_generative_model_classes:
             __lowerCamelCase            =    model_class(a       )

             __lowerCamelCase            =    model.generate(a       ).sequences
             self.assertEqual(generation_outputs.shape[-1]    ,							a       )

             __lowerCamelCase            =    jit(model.generate       )
             __lowerCamelCase            =    jit_generate(a       ).sequences

             self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_input_ids_and_config()

         # pad attention mask on the left
         __lowerCamelCase            =    attention_mask.at[(0, 0)].set(0       )

         __lowerCamelCase            =    False
         __lowerCamelCase            =    max_length

         for model_class in self.all_generative_model_classes:
             __lowerCamelCase            =    model_class(a       )

             __lowerCamelCase            =    model.generate(a    ,							attention_mask=a       ).sequences
             self.assertEqual(generation_outputs.shape[-1]    ,							a       )

             __lowerCamelCase            =    jit(model.generate       )
             __lowerCamelCase            =    jit_generate(a    ,							attention_mask=a       ).sequences

             self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_input_ids_and_config()

         # pad attention mask on the left
         __lowerCamelCase            =    attention_mask.at[(0, 0)].set(0       )

         __lowerCamelCase            =    True
         __lowerCamelCase            =    max_length

         for model_class in self.all_generative_model_classes:
             __lowerCamelCase            =    model_class(a       )

             __lowerCamelCase            =    model.generate(a    ,							attention_mask=a       ).sequences
             self.assertEqual(generation_outputs.shape[-1]    ,							a       )

             __lowerCamelCase            =    jit(model.generate       )
             __lowerCamelCase            =    jit_generate(a    ,							attention_mask=a       ).sequences

             self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_input_ids_and_config()

         # pad attention mask on the left
         __lowerCamelCase            =    attention_mask.at[(0, 0)].set(0       )

         __lowerCamelCase            =    2
         __lowerCamelCase            =    max_length

         for model_class in self.all_generative_model_classes:
             __lowerCamelCase            =    model_class(a       )

             __lowerCamelCase            =    model.generate(a    ,							attention_mask=a       ).sequences
             self.assertEqual(generation_outputs.shape[-1]    ,							a       )

             __lowerCamelCase            =    jit(model.generate       )
             __lowerCamelCase            =    jit_generate(a    ,							attention_mask=a       ).sequences

             self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()       )




@require_flax
class        a__ (  unittest.TestCase					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''       )
         __lowerCamelCase            =    FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''       )

         __lowerCamelCase            =    '''Hello world'''
         __lowerCamelCase            =    tokenizer(a    ,							return_tensors='''np'''       ).input_ids

         # typos are quickly detected (the correct argument is `do_sample`)
         with self.assertRaisesRegex(a    ,							'''do_samples'''       ):
             model.generate(a    ,							do_samples=a       )

         # arbitrary arguments that will not be used anywhere are also not accepted
         with self.assertRaisesRegex(a    ,							'''foo'''       ):
             __lowerCamelCase            =    {'''foo''': '''bar'''}
             model.generate(a    ,							**a       )

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase__							)							,    UpperCamelCase__							)
    return number - int(UpperCamelCase__							)


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))
